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Cycle frequency in standard Rock–Paper–Scissors games: Evidence from experimental economics

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  • Xu, Bin
  • Zhou, Hai-Jun
  • Wang, Zhijian

Abstract

The Rock–Paper–Scissors (RPS) game is a widely used model system in game theory. Evolutionary game theory predicts the existence of persistent cycles in the evolutionary trajectories of the RPS game, but experimental evidence has remained to be rather weak. In this work, we performed laboratory experiments on the RPS game and analyzed the social-state evolutionary trajectories of twelve populations of N=6 players. We found strong evidence supporting the existence of persistent cycles. The mean cycling frequency was measured to be 0.029±0.009 period per experimental round. Our experimental observations can be quantitatively explained by a simple non-equilibrium model, namely the discrete-time logit dynamical process with a noise parameter. Our work therefore favors the evolutionary game theory over the classical game theory for describing the dynamical behavior of the RPS game.

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  • Xu, Bin & Zhou, Hai-Jun & Wang, Zhijian, 2013. "Cycle frequency in standard Rock–Paper–Scissors games: Evidence from experimental economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4997-5005.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:20:p:4997-5005
    DOI: 10.1016/j.physa.2013.06.039
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    Citations

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    Cited by:

    1. Mäs, Michael & Nax, Heinrich H., 2016. "A behavioral study of “noise” in coordination games," Journal of Economic Theory, Elsevier, vol. 162(C), pages 195-208.
    2. Zhijian Wang & Bin Xu, 2014. "Cycling in stochastic general equilibrium," Papers 1410.8432, arXiv.org.
    3. Mäs, Michael & Nax, Heinrich H., 2016. "A behavioral study of “noise” in coordination games," LSE Research Online Documents on Economics 65422, London School of Economics and Political Science, LSE Library.
    4. Wang Zhijian, 2022. "Game Dynamics Structure Control by Design: an Example from Experimental Economics," Papers 2203.06088, arXiv.org.
    5. Wang, Yijia & Chen, Xiaojie & Wang, Zhijian, 2017. "Testability of evolutionary game dynamics based on experimental economics data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 455-464.
    6. Wang, Zhijian & Zhu, Siqian & Xu, Bin, 2013. "A Comment on "Cycles and Instability in a Rock-Paper-Scissors Population Game: A Continuous Time Experiment"," MPRA Paper 51691, University Library of Munich, Germany.
    7. Umezuki, Yosuke, 2018. "Bifurcation analysis of the rock–paper–scissors game with discrete-time logit dynamics," Mathematical Social Sciences, Elsevier, vol. 95(C), pages 54-65.
    8. Zhijian Wang & Yanran Zhou & Jaimie W. Lien & Jie Zheng & Bin Xu, 2016. "Extortion Can Outperform Generosity in the Iterated Prisoners' Dilemma," Levine's Bibliography 786969000000001297, UCLA Department of Economics.
    9. Benjamin J. Dyson, 2019. "Behavioural Isomorphism, Cognitive Economy and Recursive Thought in Non-Transitive Game Strategy," Games, MDPI, vol. 10(3), pages 1-14, August.
    10. Erik Brockbank & Edward Vul, 2021. "Formalizing Opponent Modeling with the Rock, Paper, Scissors Game," Games, MDPI, vol. 12(3), pages 1-20, September.
    11. Wenjun Hu & Haiyan Tian & Gang Zhang, 2019. "Bifurcation Analysis of Three-Strategy Imitative Dynamics with Mutations," Complexity, Hindawi, vol. 2019, pages 1-8, October.
    12. Wang Yijia & Wang Zhijian, 2023. "Pulse in collapse: a game dynamics experiment," Papers 2302.09336, arXiv.org.
    13. repec:cla:levarc:786969000000001297 is not listed on IDEAS
    14. Xin, C. & Yang, G. & Huang, J.P., 2017. "Ising game: Nonequilibrium steady states of resource-allocation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 666-673.
    15. Kabir, K.M. Ariful & Tanimoto, Jun, 2021. "The role of pairwise nonlinear evolutionary dynamics in the rock–paper–scissors game with noise," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    16. Mahdi Hajihashemi & Keivan Aghababaei Samani, 2022. "Multi-strategy evolutionary games: A Markov chain approach," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-17, February.

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